Crop quality sensor based on specular reflectance

• US 9,723,784 B2
• Filed: 09/14/2015
• Issued: 08/08/2017
• Est. Priority Date: 09/12/2014
• Status: Active Grant

• Current Assignee: Intelligent Agricultural Solutions LLC
• Original Assignee: Appareo Systems LLC (AGCO Corporation)
• Inventors: Bremer, Marshall T., Butts, Nicholas L.
• Primary Examiner(s): Lee, Jonathan S
• Application Number:
  US14/853,978
• Publication Number:
  US 20160078304A1
• Time in Patent Office: 694 Days
• CPC Class Codes: A01D 41/127 Control or measuring arrangements specially adapted for combines
• CPC Class Codes: A01D 41/1271 for measuring crop flow
• CPC Class Codes: A01D 41/1272 for measuring grain flow
• CPC Class Codes: A01D 41/1273 for measuring grain loss
• CPC Class Codes: A01D 41/1274 for drives
• CPC Class Codes: A01D 41/1276 for cleaning mechanisms
• CPC Class Codes: A01D 41/1277 for measuring grain quality
• CPC Class Codes: A01D 57/12 Rotating rakes
• CPC Class Codes: B65G 43/08 Control devices operated by article or material being fed, conveyed or discharged and controlling the discharging devices B65G47/42
• CPC Class Codes: G01B 7/14 for measuring distance or clearance between spaced objects or spaced apertures G01B7/30 takes precedence
• CPC Class Codes: G01G 19/08 for incorporation in vehicles
• CPC Class Codes: G01G 19/12 having electrical weight-sensitive devices
• CPC Class Codes: G01L 1/00 Measuring force or stress, in general measuring force due to impact G01L5/00
• CPC Class Codes: G01N 33/0098 Plants or trees wood G01N33/46
• CPC Class Codes: G01S 13/865 Combination of radar systems with lidar systems
• CPC Class Codes: G01S 13/867 Combination of radar systems with cameras
• CPC Class Codes: G01S 13/87 Combinations of radar systems, e.g. primary radar and secondary radar
• CPC Class Codes: G01S 13/88 Radar or analogous systems specially adapted for specific applications electromagnetic prospecting or detecting of objects, e.g. near-field detection, G01V3/00
• CPC Class Codes: G01S 17/04 Systems determining the presence of a target
• CPC Class Codes: G01S 17/88 Lidar systems specially adapted for specific applications
• CPC Class Codes: G01S 17/89 : for mapping or imaging
• CPC Class Codes: G01S 7/411 : Identification of targets based on measurements of radar reflectivity G01S7/415 takes precedence
• CPC Class Codes: G06T 2207/10004 : Still image; Photographic image
• CPC Class Codes: G06T 2207/10024 : Color image
• CPC Class Codes: G06T 2207/30128 : Food products
• CPC Class Codes: G06T 7/0004 : Industrial image inspection
• CPC Class Codes: G06T 7/11 : Region-based segmentation
• CPC Class Codes: G06T 7/136 : involving thresholding
• CPC Class Codes: G06V 20/56 : exterior to a vehicle by using sensors mounted on the vehicle
• CPC Class Codes: G06V 20/68 : Food, e.g. fruit or vegetables
• CPC Class Codes: H04N 23/56 : provided with illuminating means
• CPC Class Codes: H04N 23/80 : Camera processing pipelines; Components thereof

Abstract

A crop quality sensor, comprising an illumination source, an imaging device, and a processor executing application software. The illumination source is shone onto a crop sample, and an image is taken with the imaging device of the illuminated crop sample. The software executing on the processor is used to analyze the image to identify the outlines of individual kernels and to identify which of those outlines contain a specular highlight, indicative that the kernel is whole and unbroken, while the absence of such a specular highlight is indicative of a broken kernel.

Claims

1. A crop quality sensor for a harvesting machine, comprising:

an illumination source;

an imaging device;

a processor;

software executing on the processor;

wherein the imaging device is mounted in a clean grain elevator of a harvesting machine such that it can capture images of a crop sample moving through the clean grain elevator;

wherein the illumination source is mounted in proximity to the imaging device and positioned for illuminating the crop sample in the clean grain elevator;

wherein the crop sample is such that individual kernels of the crop sample have a shiny outer casing and a dull inner surface when broken open;

wherein an image is taken with the imaging device of the illuminated crop sample;

wherein the software is executing on the processor;

wherein the software is used to analyze the image to identify outlines of individual kernels and to identify which of those outlines contain a specular highlight; and

wherein a presence of a specular highlight within an outline is indicative that the kernel is whole and unbroken and an absence of such a specular highlight is indicative of a broken kernel.

2. The crop quality sensor of claim 1, wherein the software is further used to identify outlier outlines that do not conform to an expected shape or size of a kernel, and which do not contain a specular highlight within the outlier outline, wherein the absence of such a specular highlight is indicative of non-crop material.

5. The crop quality sensor of claim 1 wherein said harvesting machine includes:

a threshing assembly with a concave and a rotor.

6. The crop quality sensor of claim 5 wherein said harvesting machine includes:

a conveyor chain extending forwardly from said harvesting machine and configured for conveying crop from a field to said threshing assembly.

7. The crop quality sensor of claim 1 wherein said harvesting machine includes:

a cleaning shoe positioned below said threshing assembly and configured for receiving crop material from said rotor; and

said cleaning shoe including crop material sensors providing an output corresponding to crop material sensed on said cleaning shoe.

8. The crop quality sensor of claim 1 wherein said crop quality sensor includes:

said processor being programmed with an algorithm for detecting damaged crop material identified by either nonstandard shapes or the absence of specular highlights within their outlines.

9. The crop quality sensor of claim 1 wherein said crop quality sensor includes:

said imaging device including an optical data capture sensor.

10. The crop quality data sensor of claim 1 wherein said crop quality sensor includes:

an imaging device including photosite array data analyzed for HUE, saturation and intensity.

11. The crop quality data sensor of claim 1, which includes a moisture sensor using near-infrared (NIR) wavelengths.

12. The crop quality data sensor of claim 1, which includes:

a radar subsystem for moisture-detection based on frequency.

3. A method of determining a quality of a crop being harvested, comprising the steps of:

mounting an imaging device in a clean grain elevator of a harvesting machine such that it can capture images of a crop sample moving through the clean grain elevator;

mounting an illumination source in proximity to the imaging device such that it is positioned for illuminating the crop sample in the clean grain elevator;

taking an image of the crop sample with the imaging device of the illuminated crop sample;

sending the captured image to a processor, wherein the processor is executing optical analysis software;

analyzing the image with the optical analysis software to determine a percentage of broken kernels present in the crop sample;

wherein the crop sample is such that individual kernels of the crop sample have a shiny outer casing and a dull inner surface when broken open;

wherein the software is used to analyze the image to identify outlines of individual kernels and to identify which of those outlines contain a specular highlight; and

wherein a presence of a specular highlight within an outline is indicative that the kernel is whole and unbroken and an absence of such a specular highlight is indicative of a broken kernel.

4. The method according to claim 3, which includes the additional steps of:

said software identifying outlier outlines that do not conform to an expected shape or size of a kernel, and which do not contain a specular highlight within the outlier outline; and

the absence of such a specular highlight indicating a non-crop material.